基于非线性几何变换的量子计算优化

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum computing offers unprecedented computational power, but realizing this potential requires sophisticated optimization techniques. Traditional optimization methods often struggle with the inherent complexity of quantum circuits, particularly when dealing with non-linear geometric transformations. This paper introduces a novel quantum computing optimization algorithm, "Nonlinear Geometry Transformation-Based Quantum Computing Optimization," designed to automatically adjust quantum computation parameters based on dynamic geometric transformations. The core mechanism leverages the unique properties of non-linear geometry to optimize quantum circuits, leading to improved efficiency and accuracy. This approach overcomes limitations of conventional methods, providing a framework for automated optimization of quantum algorithms across a diverse range of tasks. The proposed algorithm's efficacy is demonstrated through a series of simulations, showcasing significant improvements in performance compared to existing optimization techniques.

DOI: 10.5281/zenodo.22252857

Related papers

自适应矩阵的非线性几何变换的量子加速计算

Jincheng Zhang

This paper explores the potential of adaptive matrix transformations to accelerate quantum computation, specifically focusing on the design of a novel quantum acceleration framework. Traditional quantum algorithms often struggle with complex geometric transformations, necessitating static approaches. We propose a new approach that leverages the inherent properties of quantum mechanics to dynamically adjust matrix operations, thereby significantly enhancing computational efficiency. The core mechanism revolves around the creation of a self-adapting matrix transformation process, allowing for efficient handling of non-linear geometric concepts within quantum circuits. This framework addresses the limitations of conventional algorithms by providing a flexible and potentially transformative path towards accelerating quantum calculations. The paper details the design of the framework, its mathematical underpinnings, and preliminary experimental validation demonstrating its efficacy in specific matrix operations. We present a case study demonstrating a significant improvement in computational complexity for a particular geometric transformation, highlighting the framework's potential for broader applicability in quantum computing.

Sep 6, 20260 citations

Dynamic Topology Geometry for Quantum Computing Speed Optimization

Jincheng Zhang

This paper explores the application of dynamic topology geometry to accelerate quantum computation. Traditional quantum algorithms often rely on manually adjusting parameters, which can be computationally expensive and limit scalability. We propose a novel algorithm that dynamically adjusts geometric parameters during the simulation process, effectively reducing the computational complexity. This approach leverages the inherent non-linearity within the system to achieve efficient and optimized performance. The core mechanism centers around a transformation of the simulation process into a parameter-adjustment iterative loop, mitigating the limitations of static optimization techniques. The investigation demonstrates significant improvements in simulation time and accuracy compared to traditional methods, highlighting the potential for substantial speedups in quantum algorithm design.

Sep 3, 20260 citations

基于量子态的自适应几何优化

Jincheng Zhang

This paper introduces a novel approach to geometric optimization leveraging the inherent quantum state properties of the problem. Traditional geometric optimization methods often require manual parameter tuning, limiting precision and requiring significant computational effort. We propose a quantum-inspired algorithm that utilizes quantum state transformations to dynamically adjust parameters during the optimization process, leading to improved geometric accuracy and robustness. The core mechanism involves mapping the problem's objective function to a quantum state space, allowing for efficient exploration of the solution space. We demonstrate the efficacy of this method through a series of numerical experiments, showcasing significant improvements in geometric accuracy compared to standard optimization techniques. The algorithm's ability to adapt to the problem's characteristics makes it a promising tool for complex geometric optimization challenges.

Aug 31, 20260 citations

基于拓扑的量子计算的自适应量子电路设计

Jincheng Zhang

Quantum computing offers the potential for exponentially faster computations compared to classical computers, particularly for complex problems. However, the design and implementation of quantum circuits remain a significant challenge due to the inherent complexity of quantum systems. This paper explores a novel approach to quantum circuit design leveraging topological principles to automate the process of circuit optimization and achieve higher computational efficiency. We propose a new framework for automatic circuit adjustment based on topological structure, enabling the system to adapt its design based on the characteristics of the task at hand. This system aims to overcome the limitations of traditional methods by offering a more flexible and robust approach to quantum circuit creation. The research demonstrates the effectiveness of this framework through a series of simulations and analysis, showcasing its ability to generate circuits with improved performance for specific quantum algorithms. The core mechanism is the application of topological topological symmetries to the circuit structure, allowing the system to dynamically adjust parameters to achieve optimal results. This work represents a significant advancement in quantum circuit design, potentially paving the way for more efficient and scalable quantum computations.

Sep 4, 20260 citations

基于自适应的量子计算的拓扑结构优化

Jincheng Zhang

Quantum computing offers the potential for revolutionary computational capabilities, but realizing this potential requires careful design of quantum circuits and algorithms. Quantum state optimization is a crucial step in this process, and traditional optimization methods often struggle with the inherent complexity of quantum systems. This paper presents a novel approach to quantum state optimization utilizing adaptive quantum state transformations, leveraging the self-adaptability of quantum states to achieve enhanced computational efficiency and precision. We detail a method that dynamically adjusts the quantum state topology to address specific challenges, resulting in improved performance compared to existing static optimization techniques. The core mechanism focuses on exploiting the quantum state's ability to evolve and adapt to changing conditions, thereby enabling the discovery of optimal quantum circuits. This work addresses the limitations of conventional optimization approaches by introducing a dynamic, self-adjusting process that directly interacts with the quantum system's inherent properties. The proposed method provides a promising path toward accelerating quantum algorithm development and enhancing the performance of various quantum computations.

Aug 29, 20260 citations

基于自适应几何的量子电路的优化算法

Jincheng Zhang

Quantum computing is a rapidly evolving field with immense potential for scientific discovery and technological advancement. However, the development of efficient quantum circuits remains a significant challenge, demanding sophisticated optimization techniques. This paper proposes a novel optimization algorithm, termed "Adaptive Geometry Optimization" (AGO), that leverages the principles of self-adaptive geometry to dynamically adjust quantum circuit parameters, leading to improved performance. Traditional optimization methods often struggle with the complex, non-linear nature of quantum circuits, while AGO offers a new approach to circuit design and optimization. The core mechanism of AGO involves systematically reshaping the circuit's geometry using a self-adaptive process, which results in enhanced circuit fidelity and ultimately, increased quantum computational capabilities. This research explores the theoretical foundations, implementation details, and preliminary experimental results of AGO, demonstrating its potential to significantly improve the performance of quantum algorithms.

Sep 4, 20260 citations